Dimensionality issue when using max pool with stride 1-CodePudding

As stated in this post, using stride one in a max-pooling layer should not decrease the dimension. However, while running some experiments, I have observed something different.

my_model = tf.keras.Sequential([ 
tf.keras.layers.Flatten(input_shape=(10,))])  
my_model.add(tf.keras.layers.Dense(16,activation=tf.identity))
my_model.add(tf.keras.layers.Reshape([16,1]))
my_model.add(tf.keras.layers.MaxPooling1D(pool_size=(4), strides=(1)))
my_model.add(tf.keras.layers.Flatten())
my_model.add(tf.keras.layers.Dense(16,activation=tf.identity))
 
my_model.summary()

Model: "sequential_24"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_35 (Flatten)         (None, 10)                0         
_________________________________________________________________
dense_41 (Dense)             (None, 16)                176       
_________________________________________________________________
reshape_18 (Reshape)         (None, 16, 1)             0         
_________________________________________________________________
max_pooling1d_18 (MaxPooling (None, 13, 1)             0         
_________________________________________________________________
flatten_36 (Flatten)         (None, 13)                0         
_________________________________________________________________
dense_42 (Dense)             (None, 16)                224       
=================================================================
Total params: 400
Trainable params: 400
Non-trainable params: 0

As one can see, using the max-pool did decrease the dimension of the data. Am I misinterpreting something?

CodePudding user response：

You forgot to add the padding='same' parameter. By default the padding is set to valid. It is tricky as one would expect to alter the padding parameter only in case of convolutions/other operations prior to the pooling one.

import tensorflow as tf
my_model = tf.keras.Sequential([ 
tf.keras.layers.Flatten(input_shape=(10,))])  
my_model.add(tf.keras.layers.Dense(16,activation=tf.identity))
my_model.add(tf.keras.layers.Reshape([16,1]))
my_model.add(tf.keras.layers.MaxPooling1D(pool_size=(4), strides=(1),padding='same'))
my_model.add(tf.keras.layers.Flatten())
my_model.add(tf.keras.layers.Dense(16,activation=tf.identity))
 
my_model.summary()   



Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_3 (Flatten)          (None, 10)                0         
_________________________________________________________________
dense_8 (Dense)              (None, 16)                176       
_________________________________________________________________
reshape_2 (Reshape)          (None, 16, 1)             0         
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 16, 1)             0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 16)                0         
_________________________________________________________________
dense_9 (Dense)              (None, 16)                272       
=================================================================
Total params: 448
Trainable params: 448
Non-trainable params: 0